arXiv subjects

Pallavi Mishra-Kalyani

Publications and source records attributed to Pallavi Mishra-Kalyani.

4 recordsLinked to original sources

arXiv2026

Implementing the principal stratum strategy for intercurrent events with survival outcomes: a tutorial

The International Council for Harmonization (ICH) E9 (R1) addendum provides the estimand framework to formulate treatment effects in a clinical trial. One of the attributes of an estimand the framework describes is intercurrent events. Among the five strategies to intercurrent events the guidance lists, the principal stratum strategy is the most conceptually and technically challenging because it defines treatment effects on unobserved strata. Its application to survival outcomes is particularly inaccessible to practitioners. This tutorial reviews the methodology and implementation of the estimand framework with the principal stratum strategy to address intercurrent events with survival outcomes. We illustrate using a clinical trial in oncology and focus on a simple case with binary treatment and a single binary intercurrent event of discontinuation of the assigned treatment. We define the causal effects and review two main methods for estimating the effects: the mixture model method and the weighting method. For each method, we elaborate the associated assumptions, models, sensitivity analysis, software and provide example R code. We conduct simulation studies that mimic the real study to study the operation characteristics of these methods.

stat.ME↗

arXiv2026

Externally Controlled Trials: A Review of Design and Borrowing Through a Causal Lens

Externally controlled trials (ECTs) are increasingly used when randomized controls are infeasible, unethical, or insufficient, including applications in rare diseases, oncology, pediatrics, and post-approval effectiveness research. Although methodological work has expanded rapidly across causal inference, Bayesian dynamic borrowing, and hybrid trial designs, the literature remains fragmented. We adopt a six-step scientific roadmap to organize modern ECT methodology in two primary settings: (i) single-arm trials that evaluate efficacy through comparison with external controls, and (ii) hybrid controlled trials that augment the internal control arm with external controls drawn from real-world data or historical studies. The roadmap clarifies causal estimands, identifiability assumptions, and how statistical parameters arise from identification, and shows how modeling and borrowing strategies trade off efficiency and robustness, especially under covariate shift and outcome drift. Within this framework, we synthesize and evaluate recent Bayesian and frequentist developments, compare their strengths, limitations, operating characteristics, and available software, and emphasize the role of sensitivity analysis. By re-framing ECT methodology through a causal lens, this work establishes a coherent foundation for integrating external data into regulatory and clinical decision-making and highlights core challenges and opportunities for future research.

stat.ME↗

arXiv2025

Sensitivity Analysis for Unmeasured Confounding in Medical Product Development and Evaluation Using Real World Evidence

The American Statistical Association Biopharmaceutical Section (ASA BIOP) scientific working group on real-world evidence (RWE) has been making continuous, extended efforts towards a goal of supporting and advancing regulatory science with respect to clinical studies intended to use real-world data for evidence generation for the purpose of medical product development and evaluation (i.e., RWE studies). In 2023, the working group published a manuscript delineating challenges and opportunities in constructing estimands for RWE studies following the framework in ICH E9(R1) guidance on estimand and sensitivity analysis. As a follow-up task, we describe the other issue, sensitivity analysis. Although the FDA's definition of RWE studies includes randomized trials using RWD as a primary source of evidence generation such as pragmatic trials, here we focus on non-randomized RWE studies and the issue of unmeasured confounding which is a major source of bias for most RWE studies. We review the availability and applicability of sensitivity analysis methods for different types of unmeasured confounding. We also provide some practical and regulatory considerations on using sensitivity analysis for such RWE studies. Then, we use a plasmode case study to demonstrate realistic ways to interpret sensitivity analysis results in supporting regulatory decision-making. We conclude with a brief discussion.

stat.ME↗

arXiv2019

Estimating the effect of PEG in ALS patients using observational data subject to censoring by death and missing outcomes

Though they may offer valuable patient and disease information that is impossible to study in a randomized trial, clinical disease registries also require special care and attention in causal inference. Registry data may be incomplete, inconsistent, and subject to confounding. In this paper we aim to address several analytical issues in estimating treatment effects that plague clinical registries such as the Emory amyotrophic lateral sclerosis (ALS) Clinic Registry. When attempting to assess the effect of a surgical insertion of a percutaneous endoscopic gastrostomy (PEG) tube on body mass index (BMI) using the data from the ALS Clinic Registry, one must combat issues of confounding, censoring by death, and missing outcome data that have not been addressed in previous studies of PEG. We propose a causal inference framework for estimating the survivor average causal effect (SACE) of PEG, which incorporates a model for generalized propensity scores to correct for confounding by pre-treatment variables, a model for principal stratification to account for censoring by death, and a model for the missing data mechanism. Applying the proposed framework to the ALS Clinic Registry Data, our analysis shows that PEG has a positive SACE on BMI at month 18 post-baseline; our results likely offer more definitive answers regarding the effect of PEG than previous studies of PEG.

stat.AP↗